Data Science Race  Man vs Machine

Two separate trends have overtaken the worldthe growing automation of every facet of society, and the recent explosion of social media! Both these trends have led to uncontrollable streams of data, mostly unstructured and in various forms like text mail, pictures, web logs, or audio clips.

In keeping up with the pace of automation, modern computers have become cheaper and more powerful. The current technology enables organizations to store massive volumes of data, reliably and cheaply without any loss of quality.

How have these events influenced popular thinking?

The major movements just described above have practically started a parallel movement in the field of data science, which has caught the public attention in the last few years and continues to storm the online hangouts. Gary King, a Harvard statistician has described the data science movement as a revolution in a 2012 edition of the New York Times. He says, Were really just getting under way. But the march of quantification, made possible by enormous new sources of data, will sweep through academia, business, and government. There is no area that is going to be untouched.

The broad conclusion that one can draw from the above quote is that very soon, each and every organization will require several positions, whose main responsibility will be to manage enterprise data for automated decision making. There is a possibility that with time, these human decisions will become obvious and may stand the chance of getting automated. But can machines ever take over human thinking?

Data science as a field of opportunity

According to Flowing Data, data scientist is a term that ranks very high on search enginesovertaking the term statistician in December 2013. Even Google trends have shown similar preferences for the term data analyst or other data science-related terms throughout 2013. This trend seems to be snowballing.

Data science, as is apparent from current online search trends, is the field of the present and future. This unique field of study enables organizations to convert their data into meaningful intelligence for competitive advantages. Industry sectors like health, finance, insurance, and non-business sectors like government or education all depend on big data analytics for improved performance. Thus data science teams will gain increasing importance in this emerging data economy. Data science, combined with the predictive power of statistics will separate the winners from the losers in tomorrows economy.

On the one hand, data is increasingly plentiful and easy to store, transfer, and analyze, Businesses now save details about every transaction and promotion. Online companies can track detailed browsing habits of their users. This presents an opportunity to create and test hypotheses about how to operate more effectively. On the other hand, this is an increasingly technical task, requiring skills in statistics, computer science, and communication.

Data scientists: The leaders of tomorrow

The fact that data scientists will be highly in demand throughout this decade has been reconfirmed by many prominent market research organizations. The McKinsey Global Institute (MGI) report has stated that by 2018, the United States alone could face a shortage of 140,000 to 190,000 people with deep analytical skills as well as 1.5 million managers and analysts with the know-how to use the analysis of big data to make effective decisions.

Although a strong case has been made for smart machines replacing data scientists, an equally plausible counter-argument can be offered by stating that software systems or tools are simply technological aids for data scientists to achieve revolutionary work. Machines can build models or conduct predictive analytics, but data scientists will still be required to customize models or make decisions based on analytical results. A better argument may be that data science needs to develop new creative thinkers, who can utilize high-end machines and tools to create data-driven models for handling day-to-day, cross-sector challenges.

Data science applications: Automobile industry

The automobile industry is a fine example for demonstrating the hidden opportunities in data science to improve the scale and scope of a business. Vast amounts of data related to oil consumption, mileage, etc., or cost data of auto financing and insurancecan all help auto manufacturers, oil companies, insurance companies, mine and analyze the data for discovering patterns and making future product decisions. In fact, the auto industry is closely related to so many other industries, that big data analytics will play a significant role in gathering insights from major streams of information to design future products and services.

The Field Guide to Data Science

Booz Allen Hamilton, a solution enabler in the field of data science, has extensive functional knowledge and experiences across a variety of industries including health and national security. In his book tiltedThe Field Guide to Data Science, Booz Allen shares rare expertise in the field of data science. Start Here for the Basics provides a refreshing overview of why data science is considered unique. Take Off the Training Wheels provides practical guidelines for demystifying complex data science problems. Guide to Analytic Selection can serve as an aid for selecting the right analytic technique in a given context. Life in the Trenches takes the reader through the work life of a data scientist. Putting it All Together showcases actual solutions created for clients.

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